In this section, we will implement the k-nearest neighbors (KNN) algorithm to build a model on our IBM attrition dataset. Of course, we are already aware from EDA that we have a class imbalance problem in the dataset at hand. However, we will not be treating the dataset for class imbalance for now as this is an entire area on its own and several techniques are available in this area and therefore out of scope for the ML ensembling topic covered in this chapter. We will, for now, consider the dataset as is and build ML models. Also, for class imbalance datasets, Kappa or precision and recall or the area under the curve of the receiver operating characteristic (AUROC) are the appropriate metrics to use. However, for simplicity, we will use accuracy as a performance metric. We will adapt 10-fold cross validation repeated...
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Book Overview & Buying
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Table Of Contents
R Machine Learning Projects
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R Machine Learning Projects
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Overview of this book
R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.
This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.
By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
Table of Contents (12 chapters)
Preface
Free Chapter
Exploring the Machine Learning Landscape
Predicting Employee Attrition Using Ensemble Models
Implementing a Jokes Recommendation Engine
Sentiment Analysis of Amazon Reviews with NLP
Customer Segmentation Using Wholesale Data
Image Recognition Using Deep Neural Networks
Credit Card Fraud Detection Using Autoencoders
Automatic Prose Generation with Recurrent Neural Networks
Winning the Casino Slot Machines with Reinforcement Learning
The Road Ahead
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